Improved Emotional Alignment of AI and Humans: Human Ratings of Emotions Expressed by Stable Diffusion v1, DALL-E 2, and DALL-E 3

• URL: http://arxiv.org/abs/2405.18510v1
• Date: Tue, 28 May 2024 18:26:57 GMT
• Title: Improved Emotional Alignment of AI and Humans: Human Ratings of Emotions Expressed by Stable Diffusion v1, DALL-E 2, and DALL-E 3
• Authors: James Derek Lomas, Willem van der Maden, Sohhom Bandyopadhyay, Giovanni Lion, Nirmal Patel, Gyanesh Jain, Yanna Litowsky, Haian Xue, Pieter Desmet,
• Abstract summary: Generative AI systems are increasingly capable of expressing emotions via text and imagery. We measure the alignment between emotions expressed by generative AI and human perceptions. We show that the alignment significantly depends upon the AI model used and the emotion itself.
• Score: 10.76478480925475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI systems are increasingly capable of expressing emotions via text and imagery. Effective emotional expression will likely play a major role in the efficacy of AI systems -- particularly those designed to support human mental health and wellbeing. This motivates our present research to better understand the alignment of AI expressed emotions with the human perception of emotions. When AI tries to express a particular emotion, how might we assess whether they are successful? To answer this question, we designed a survey to measure the alignment between emotions expressed by generative AI and human perceptions. Three generative image models (DALL-E 2, DALL-E 3 and Stable Diffusion v1) were used to generate 240 examples of images, each of which was based on a prompt designed to express five positive and five negative emotions across both humans and robots. 24 participants recruited from the Prolific website rated the alignment of AI-generated emotional expressions with a text prompt used to generate the emotion (i.e., "A robot expressing the emotion amusement"). The results of our evaluation suggest that generative AI models are indeed capable of producing emotional expressions that are well-aligned with a range of human emotions; however, we show that the alignment significantly depends upon the AI model used and the emotion itself. We analyze variations in the performance of these systems to identify gaps for future improvement. We conclude with a discussion of the implications for future AI systems designed to support mental health and wellbeing.

Related papers

• MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis [53.012111671763776]
  This study introduces MEMO-Bench, a comprehensive benchmark consisting of 7,145 portraits, each depicting one of six different emotions. Results demonstrate that existing T2I models are more effective at generating positive emotions than negative ones. Although MLLMs show a certain degree of effectiveness in distinguishing and recognizing human emotions, they fall short of human-level accuracy.
  arXiv  Detail & Related papers  (2024-11-18T02:09:48Z)
• Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
  We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN) EDEN recognizes emotion and causes in an explicitly thinking way. It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
  arXiv  Detail & Related papers  (2024-06-07T08:58:29Z)
• ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains [61.50113532215864]
  Causal Emotion Entailment (CEE) aims to identify the causal utterances in a conversation that stimulate the emotions expressed in a target utterance. Current works in CEE mainly focus on modeling semantic and emotional interactions in conversations. We introduce a step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to infer the stimulus from the target emotional expressions in conversations.
  arXiv  Detail & Related papers  (2024-05-17T15:45:08Z)
• Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
  We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system. We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
  arXiv  Detail & Related papers  (2024-04-03T08:48:50Z)
• emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation [42.29118614670941]
  We propose emotion2vec, a universal speech emotion representation model. emotion2vec is pre-trained on unlabeled emotion data through self-supervised online distillation. It outperforms state-of-the-art pre-trained universal models and emotion specialist models.
  arXiv  Detail & Related papers  (2023-12-23T07:46:55Z)
• The Good, The Bad, and Why: Unveiling Emotions in Generative AI [73.94035652867618]
  We show that EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain.
  arXiv  Detail & Related papers  (2023-12-18T11:19:45Z)
• A Portrait of Emotion: Empowering Self-Expression through AI-Generated Art [0.0]
  We investigated the potential and limitations of generative artificial intelligence (AI) in reflecting the authors' cognitive processes through creative expression. Results show a preference for images based on the descriptions of the authors' emotions over the main events. Our research framework with generative AIs can help design AI-based interventions in related fields.
  arXiv  Detail & Related papers  (2023-04-26T06:54:53Z)
• Speech Synthesis with Mixed Emotions [77.05097999561298]
  We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector.
  arXiv  Detail & Related papers  (2022-08-11T15:45:58Z)
• HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence [9.153146173929935]
  Next-generation artificial emotional intelligence (AEI) is taking center stage to address users' desire for deeper, more meaningful human-machine interaction. Unlike theory of emotion, which has been the historical focus in psychology, emotion models are a descriptive tools. This work has broad implications in social robotics, human-machine interaction, mental healthcare, and computational psychology.
  arXiv  Detail & Related papers  (2022-06-15T15:21:30Z)
• Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator [18.711852474600143]
  We study the problem of positive emotion elicitation, which aims to generate responses that can elicit positive emotion of the user. We propose a weakly supervised Emotion Eliciting Machine (EEM) to address this problem. EEM outperforms the existing models in generating responses with positive emotion elicitation.
  arXiv  Detail & Related papers  (2021-05-18T03:19:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.